The present invention relates generally to coronary calcium scoring. More specifically, the present invention relates to methods, software, and systems that generate a patient-specific signal threshold for improving the sensitivity and accuracy of coronary calcium scoring, although the same method could be used in other applications such as contrast enhancement studies, time density analysis, and the like.
Coronary artery disease is a leading cause of death in the United States and other industrialized nations. Unfortunately, diagnosis of coronary artery disease is generally not made until the patient becomes symptomatic. By that time, the coronary artery disease may be advanced or the patient may have already had a myocardial infarction (i.e., heart attack).
One promising non-invasive method of detecting coronary artery disease in its early stage is “coronary calcium scoring,” which can measure a level of the patient's coronary calcium in the patient's coronary arteries. Although the current orthodoxy is that the rupture of soft plaque and subsequent thrombus formation is the major precursor of acute coronary events, in most individuals it is believed that the coronary calcium measurement is also a valid surrogate or indicator of total plaque burden, including soft plaque.
Calcium scoring is quickly becoming a major focus in the effort to assess risk for coronary heart disease, to monitor progression of plaque development, and to potentially assess therapies and interventions designed to reduce mortality from coronary heart disease. (See Rumberger J. A. et al, “Electron Beam Computed Tomographic Coronary Calcium Scanning: A Review and Guidelines for Use in Asymptomatic Persons,” Mayo Clinic Proc. 1999; 74:243–252 and Schmermund A., et al, “An Algorithm for Noninvasive Identification of Angiographic Three-Vessel and/or left Main Coronary Artery Disease in Symptomatic Patients,” J. Am. Coll. Cardiology 1999; 33:444–452, the complete disclosure of which are incorporated herein by reference). While calcium scoring was initially concentrated in finding the 10–25% of the population with high calcium scores and at high risk for a short term coronary event, the focus is now shifting to finding early disease and to plan preventive treatments. Some forms of treatment are benign, such as diet and exercise, others include cholesterol-lowering drugs such as the statins, which are costly and have side effects. Early identification is becoming the frontier of coronary calcium scoring.
The assessment of risk from coronary calcium is generally a multi-step process: First, a patient is imaged, typically using a CT scanner. The multi-slice images are analyzed to identify the calcium and thereafter a calcium burden is quantitated by a “scoring” algorithm, most commonly with the Agatston scale. (See Agatston A. S. et al, “Quantification of Coronary Artery Calcium Using Ultrafast Computed Tomography,” J. Am. Coll. Cardiology 1990; 15:827–832, the complete disclosure of which is incorporated herein by reference). Next, the measured calcium burden, age, gender of the individual, and other factors are used to rank the individual against his or her age-matched cohort to calculate the patient's risk for a coronary event.
While conventional calcium scoring methods are effective in estimating the amount of calcium in the patient's coronary arteries, improvements are still needed. For example, conventional coronary calcium scoring is generally performed using a fixed attenuation threshold (typically about 130 HU for EBCT and between 90 HU and 130 HU for CT) to allow for differentiating between calcium deposits and the surrounding soft-tissue.
Because the calcium scoring procedure has to be robust, this threshold is set high enough so that in the worst noise case, the score is reliable in that it represents a calcium deposit rather than noise fluctuations. Unfortunately, the high threshold also means that for most patients being studied and for most slices being evaluated, the threshold is set higher than necessary and true lesions may be missed (e.g., false negatives). On the other hand, if the signal threshold is set too low, noise or other artifacts may be thought to be lesions and “false positives” may be scored. Both false negatives and false positives are undesirable since both affect the accuracy of the patient's calcium score and consequent recommendations.
Because the fixed attenuation threshold does not compensate for noise differences in the specific image scanner being used (e.g. beam hardening artifacts, scanner imperfections, differences between scanner models, and the like) and variances of the tissue density for each patient, the fixed threshold is generally set high enough to prevent false-positive detections, e.g., high enough so that the noise in the image is not confused with the calcium deposits in almost all patients. Importantly, noise in the images may cause some or all of the early lesions to be missed. Since the early lesions are smaller in size and generate a weaker signal in the slice image, these weaker signals can be easily hidden by being above the noise levels, but below the attenuation threshold used to differentiate the “calcium” deposits from the noise and surrounding tissue.
One proposed alternative method to using a fixed threshold is described in Raggi et al., “Calcium Scoring of the Coronary Artery by Electron Beam CT: How to Apply an Individual Attenuation Threshold,” A. J. R. Vol. 178, February 2002, pp. 497–502. Raggi et al. suggest the use of an individualized threshold setting instead of the fixed threshold of 130 HU for the Agatston and volume scores using an Electron Beam CT scanner. Raggi et al. describes setting the threshold at three standard deviations above the background level. From such calculations, Raggi et al. concludes that a threshold of 120 HU (which equals the background level plus three standard deviations) is more appropriate than the more common threshold of 130 HU.
The threshold calculated by Raggi et al. is expected to have a certain number of “false positives,” (e.g., noise that is above the threshold) and for different values of the multiplier of the standard deviation, the false positives would vary in a predictable manner. Whatever the multiplier is, it is selected ahead of time for all slices in all patients.
Unfortunately, Raggi et al's proposed solution still has problems which makes it difficult to produce a reliable estimate of the false positives and use the highest sensitivity (i.e., lowest threshold) compatible with the number of false positives that are deemed acceptable. The major problem has to do with understanding what the standard deviation signifies. Raggi et al.'s expectation of three standard deviations to yield a certain number of false positives is not operational since the number of false positives will be different depending on the size of the lesion that is analyzed. Given the data analysis tools provided by a conventional CT scanner, it is difficult to know what level of false positives that will be generated for any one size lesion.
These considerations are based on the behavior of CT scanner noise (e.g., standard deviation). Scanner imperfections, such as bad detectors, small changes in detector behavior after calibration, bone and air in the subject, motion and reconstruction algorithms, all introduce noise that is not stochastic, i.e., its behavior is not predicted by statistics. Such noise is called structured noise. The frequency distribution of the structured noise will depend on the particulars of how it is being generated and is unpredictable. The effect of such noise will show up where it has repetition patterns (e.g., at its spatial frequency), so a calculation of a standard deviation for single pixels will not necessarily reflect the noise present in the image at the lesion sizes of interest. Because such structured noise is common in CT imaging, using a multiple of the standard deviation for setting thresholds does not provide a reliable provider of the false positive rate that will be achieved.
For the above reasons, what are needed are improved methods, software, and devices which improve coronary calcium scoring.